Automatic 3D Modeling System and Method

ABSTRACT

An automatic 3D modeling system and method are described in which a 3D model may be generated from a picture or other image. For example, a 3D model for a face of a person may be automatically generated. The system and method also permits gestures/behaviors associated with a 3D model to automatically generated so that the gestures/behaviors may be applied to any 3D models.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.12/099,098, filed on Apr. 7, 2008, and entitled “Automatic 3D Modeling,”which in turn claims priority under 35 USC §120 from U.S. Utility patentapplication Ser. No. 11/354,511, filed on Feb. 14, 2006, and entitled“Automatic 3D Modeling System and Method” which in turn claims priorityunder 35 USC §120 from U.S. Utility patent application Ser. No.10/219,041, filed on Aug. 13, 2002, and entitled “Automatic 3D ModelingSystem and Method” which in turn claims priority under 35 USC 119(e)from U.S. Provisional Patent Application Ser. No. 60/312,384 filed onAug. 14, 2001 and entitled “Automatic 3D Modeling System And Method,”all of which are incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The present invention is related to 3D modeling systems and methods andmore particularly, to a system and method that merges automaticimage-based model generation techniques with interactive real-timecharacter orientation techniques to provide rapid creation of virtual 3Dpersonalities.

BACKGROUND

There are many different techniques for generating an animation of athree dimensional object on a computer display. Originally, the animatedfigures (for example, the faces) looked very much like woodencharacters, since the animation was not very good. In particular, theuser would typically see an animated face, yet its features andexpressions would be static. Perhaps the mouth would open and close, andthe eyes might blink, but the facial expressions, and the animation ingeneral, resembled a wood puppet. The problem was that these animationswere typically created from scratch as drawings, and were not renderedusing an underlying 3D model to capture a more realistic appearance, sothat the animation looked unrealistic and not very life-like. Morerecently, the animations have improved so that a skin may cover thebones of the figure to provide a more realistic animated figure.

While such animations are now rendered over one or more deformationgrids to capture a more realistic appearance for the animation, oftenthe animations are still rendered by professional companies andredistributed to users. While this results in high-quality animations,it is limited in that the user does not have the capability to customizea particular animation, for example, of him or herself, for use as avirtual personality. With the advance features of the Internet or theWorld Wide Web, these virtual personas will extend the capabilities andinteraction between users. It would thus be desirable to provide a 3Dmodeling system and method which enables the typical user to rapidly andeasily create a 3D model from an image, such as a photograph, that isuseful as a virtual personality.

Typical systems also required that, once a model was created by theskilled animator, the same animator was required to animate the variousgestures that you might want to provide for the model. For example, theanimator would create the animation of a smile, a hand wave or speakingwhich would then be incorporated into the model to provide the modelwith the desired gestures. The process to generate the behavior/gesturedata is slow and expensive and requires a skilled animator. It isdesirable to provide an automatic mechanism for generating gestures andbehaviors for models without the assistance of a skilled animator. It isto these ends that the present invention is directed.

SUMMARY

Broadly, the invention utilizes image processing techniques, statisticalanalysis and 3D geometry deformation to allow photo-realistic 3D modelsof objects, such as the human face, to be automatically generated froman image (or from multiple images). For example, for the human face,facial proportions and feature details from a photograph (or series ofphotographs) are identified and used to generate an appropriate 3Dmodel. Image processing and texture mapping techniques also optimize howthe photograph(s) is used as detailed, photo-realistic texture for the3D model.

In accordance with another aspect of the invention, a gesture of theperson may be captured and abstracted so that it can be applied to anyother model. For example, the animated smile of a particular person maybe captured. The smile may then be converted into feature space toprovide an abstraction of the gesture. The abstraction of the gesture(e.g., the movements of the different portions of the model) arecaptured as a gesture. The gesture may then be used for any other model.Thus, in accordance with the invention, the system permits thegeneration of a gesture model that may be used with other models.

In accordance with the invention, a method for generating a threedimensional model of an object from an image is provided. The methodcomprises determining the boundary of the object to be modeled anddetermining the location of one or more landmarks on the object to bemodeled. The method further comprises determining the scale andorientation of the object in the image based on the location of thelandmarks, aligning the image of the object with the landmarks with adeformation grid, and generating a 3D model of the object based on themapping of the image of the object to the deformation grid.

In accordance with another aspect of the invention, a computerimplemented system for generating a three dimension model of an image isprovided. The system comprises a three dimensional model generationmodule further comprising instructions that receive an image of anobject and instructions that automatically generate a three dimensionalmodel of the object. The system further comprises a gesture generationmodule further comprising instructions for generating a feature spaceand instructions for generating a gesture object corresponding to agesture of the object so that the gesture behavior may be applied toanother model of an object.

In accordance with yet another aspect of the invention, a method forautomatically generating an automatic gesture model is provided. Themethod comprises receiving an image of an object performing a particulargesture and determining the movements associated with the gesture fromthe movement of the object to generate a gesture object wherein thegesture object further comprises a coloration change variable storingthe change of coloration that occur during the gesture, a twodimensional change variable storing the change of the surface that occurduring the gesture and a three dimensional change variable storing thechange of the vertices associated with the object during the gesture.

In accordance with yet another aspect of the invention, a gesture objectdata structure that stores data associated with a gesture for an objectis provided. The gesture object comprises a texture change variablestoring changes in coloration of a model during a gesture, a texture mapchange variable storing changes in the surface of the model during thegesture, and a vertices change variable storing changes in the verticesof the model during the gesture wherein the texture change variable, thetexture map change variable and the vertices change variable permit thegesture to be applied to another model having a texture and vertices.The gesture object data structure stored its data in a vector spacewhere coloration, surface motion and 3D motion may be used by manyindividual instances of the model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart describing a method for generating a 3D model of ahuman face;

FIG. 2 is a diagram illustrating an example of a computer system whichmay be used to implement the 3D modeling method in accordance with theinvention;

FIG. 3 is a block diagram illustrating more details of the 3D modelgeneration system in accordance with the invention;

FIG. 4 is an exemplary image of a person's head that may be loaded intothe memory of a computer during an image acquisition process;

FIG. 5 illustrates the exemplary image of FIG. 4 with an opaquebackground after having processed the image with a “seed fill”operation;

FIG. 6 illustrates the exemplary image of FIG. 5 having dashed linesindicating particular bound areas about the locations of the eyes;

FIG. 7 illustrates the exemplary image of FIG. 6 with the high contrastluminance portion of the eyes identified by dashed lines;

FIG. 8 is an exemplary diagram illustrating various landmark locationpoints for a human head;

FIG. 9 illustrates an example of a human face 3D model in accordancewith the invention;

FIGS. 10A-10D illustrate respective deformation grids that can be usedto generate a 3D model of a human head;

FIG. 10E illustrates the deformation grids overlaid upon one another;

FIG. 11 is a flowchart illustrating the automatic gesture behaviorgeneration method in accordance with the invention;

FIGS. 12A and 12B illustrate an exemplary pseudo-code for performing theimage processing techniques of the invention;

FIGS. 13A and 13B illustrate an exemplary work flow process forautomatically generating a 3D model in accordance with the invention;

FIGS. 14A and 14B illustrate an exemplary pseudo-code for performing theautomatic gesture behavior model in accordance with the invention;

FIG. 15 illustrates an example of a base 3D model for a first model,Kristen;

FIG. 16 illustrates an example of a base 3D model for a second model,Ellie;

FIG. 17 is an example of the first model in a neutral gesture;

FIG. 18 is an example of the first model in a smile gesture;

FIG. 19 is an example of a smile gesture map generated from the neutralgesture and the smile gesture of the first model;

FIG. 20 is an example of the feature space with both the models overlaidover each other;

FIG. 21 is an example of a neutral gesture for the second model; and

FIG. 22 is an example of the smile gesture, generated from the firstmodel, being applied to the second model to generate a smile gesture inthe second model.

DETAILED DESCRIPTION

While the invention has a greater utility, it will be described in thecontext of generating a 3D model of the human face and gesturesassociated with the human fact. Those skilled in the art recognize thatany other 3D models and gestures can be generated using the principlesand techniques described herein, and that the following is merelyexemplary of a particular application of the invention and the inventionis not limited to the facial models described herein.

To generate a 3D model of the human face, the invention performs aseries of complex image processing techniques to determine a set oflandmark points 10 which serve as guides for generating the 3D model.FIG. 1 is a flow chart describing a preferred algorithm for generating a3D model of a human face. With reference to FIG. 1, an image acquisitionprocess (Step 1) is used to load a photograph(s) (or other image) of ahuman face (for example, a “head shot”) into the memory of a computer.Images may be loaded as JPEG images, however, other image type formatsmay be used without departing from the invention. Images can be loadedfrom a diskette, downloaded from the Internet, or otherwise loaded intomemory using known techniques so that the image processing techniques ofthe invention can be performed on the image in order to generate a 3Dmodel.

Since different images may have different orientations, the properorientation of the image should be determined by locating and gradingappropriate landmark points 10. Determining the image orientation allowsa more realistic rendering of the image onto the deformation grids.Locating the appropriate landmark points 10 will now be described indetail.

Referring to FIG. 1, to locate landmark points 10 on an image, a “seedfill” operation may be performed (Step 2) on the image to eliminate thevariable background of the image so that the boundary of the head (inthe case of a face) can be isolated on the image. FIG. 4 is an exemplaryimage 20 of a person's head that may be loaded into the memory of thecomputer during the image acquisition process (Step 1, FIG. 1). A “seedfill” operation (Step 2, FIG. 1) is a well-known recursive paintfilloperation that is accomplished by identifying one or more points 22 inthe background 24 of the image 20 based on, for example, color andluminosity of the point(s) 22 and expand a paintfill zone 26 outwardlyfrom the point(s) 22 where the color and luminosity are similar. The“seed fill” operation successfully replaces the color and luminescentbackground 24 of the image with an opaque background so that, theboundary of the head can be more easily determined.

Referring again to FIG. 1, the boundary of the head 30 can be determined(Step 3), for example, by locating the vertical center of the image(line 32) and integrating across a horizontal area 34 from thecenterline 32 (using a non-fill operation) to determine the width of thehead 30, and by locating the horizontal center of the image (line 36)and integrating across a vertical area 38 from the centerline 36 (usinga non-fill operation) to determine the height of the head 30. In otherwords, statistically directed linear integration of a field of pixelswhose values differ based on the presence of an object or the presenceof a background is performed. This is shown in FIG. 5 which shows theexemplary image 20 of FIG. 4 with an opaque background 24.

Returning again to FIG. 1, upon determining the width and height of thehead 30, the bounds of the head 30 can be determined by usingstatistical properties of the height of the head 30 and the knownproperties of the integrated horizontal area 34 and top of the head 30.Typically, the height of the head will be approximately ⅔ of the imageheight and the width of the head will be approximately ⅓ of the imagewidth. The height of the head may also be 1.5 times the width of thehead which is used as a first approximation.

Once the bounds of the head 30 are determined, the location of the eyes40 can be determined (Step 4). Since the eyes 40 are typically locatedon the upper half of the head 30, a statistical calculation can be usedand the head bounds can be divided into an upper half 42 and a lowerhalf 44 to isolate the eye bound areas 46 a, 46 b. The upper half of thehead bounds 42 can be further divided into right and left portions 46 a,46 b to isolate the left and right eyes 40 a, 40 b, respectively. Thisis shown in detail in FIG. 6 which shows the exemplary image 20 of FIG.4 with dashed lines indicating the particular bound areas.

Referring yet again to FIG. 1, the centermost region of each eye 40 a,40 b can be located (Step 5) by identifying a circular region 48 of highcontrast luminance within the respective eye bounds 46 a, 46 b. Thisoperation can be recursively performed outwardly from the centermostpoint 48 over the bounded area 46 a, 46 b and the results can be gradedto determine the proper bounds of the eyes 40 a, 40 b. FIG. 7 shows theexemplary image of FIG. 6 with the high contrast luminance portion ofthe eyes identified by dashed lines.

Referring again to FIG. 1, once the eyes 40 a, 40 b have beenidentified, the scale and orientation of the head 30 can be determined(Step 6) by analyzing a line 50 connecting the eyes 40 a, 40 b todetermine the angular offset of the line 50 from a horizontal axis ofthe screen. The scale of the head 30 can be derived from the width ofthe bounds according to the following formula: width of bound/width ofmodel.

After determining the above information, the approximate landmark points10 on the head 30 can be properly identified. Preferred landmark points10 include a) outer head bounds 60 a, 60 b, 60 c; b) inner head bounds62 a, 62 b, 62 c, 62 d; c) right and left eye bounds 64 a-d, 64 w-z,respectively; d) corners of nose 66 a, 66 b; and e) corners of mouth 68a, 68 b (mouth line), however, those skilled in the art recognize thatother landmark points may be used without departing from the invention.FIG. 8 is an exemplary representation of the above landmark points shownfor the image of FIG. 4.

Having determined the appropriate landmark locations 10 on the head 30,the image can be properly aligned with one or more deformation grids(described below) that define the 3D model 70 of the head (Step 7). Thefollowing describes some of the deformation grids that may be used todefine the 3D model 70, however, those skilled in the art recognize thatthese are merely exemplary of certain deformation grids that may be usedto define the 3D model and that other deformation grids may be usedwithout departing from the invention. FIG. 9 illustrates an example of a3D model of a human face generated using the 3D model generation methodin accordance with the invention. Now, more details of the 3D modelgeneration system will be described.

FIG. 2 illustrates an example of a computer system 70 in which the 3Dmodel generation method and gesture model generation method may beimplemented. In particular, the 3D model generation method and gesturemodel generation method may be implemented as one or more pieces ofsoftware code (or compiled software code) which are executed by acomputer system. The methods in accordance with the invention may alsobe implemented on a hardware device in which the method in accordancewith the invention are programmed into a hardware device. Returning toFIG. 2, the computer system 70 shown is a personal computer system. Theinvention, however, may be implemented on a variety of differentcomputer systems, such as client/server systems, server systems,workstations, etc. . . . and the invention is not limited toimplementation on any particular computer system. The illustratedcomputer system may include a display device 72, such as a cathode raytube or LCD, a chassis 74 and one or more input/output devices, such asa keyboard 76 and a mouse 78 as shown, which permit the user to interactwith the computer system. For example, the user may enter data orcommands into the computer system using the keyboard or mouse and mayreceive output data from the computer system using the display device(visual data) or a printer (not shown), etc. The chassis 74 may housethe computing resources of the computer system and may include one ormore central processing units (CPU) 80 which control the operation ofthe computer system as is well known, a persistent storage device 82,such as a hard disk drive, an optical disk drive, a tape drive and thelike, that stores the data and instructions executed by the CPU evenwhen the computer system is not supplied with power and a memory 84,such as DRAM, which temporarily stores data and instructions currentlybeing executed by the CPU and loses its data when the computer system isnot being powered as is well known. To implement the 3D model generationand gesture generation methods in accordance with the invention, thememory may store a 3D modeler 86 which is a series of instructions anddata being executed by the CPU 80 to implement the 3D model and gesturegeneration methods described above. Now, more details of the 3D modelerwill be described.

FIG. 3 is a diagram illustrating more details of the 3D modeler 86 shownin FIG. 2. In particular, the 3D modeler includes a 3D model generationmodule 88 and a gesture generator module 90 which are each implementedusing one or more computer program instructions. The pseudo-code thatmay be used to implement each of these modules is shown in FIGS. 12A-12Band FIGS. 14A and 14B. As shown in FIG. 3, an image of an object, suchas a human face is input into the system as shown. The image is fed intothe 3D model generation module as well as the gesture generation moduleas shown. The output from the 3D model generation module is a 3D modelof the image which has been automatically generated as described above.The output from the gesture generation module is one or more gesturemodels which may then be applied to and used for any 3D model includingany model generate by the 3D model generation module. The gesturegenerator is described in more detail below with reference to FIG. 11.In this manner, the system permits 3D models of any object to be rapidlygenerated and implemented. Furthermore, the gesture generator permitsone or more gesture models, such as a smile gesture, a hand wave, etc. .. . ) to be automatically generated from a particular image. Theadvantage of the gesture generator is that the gesture models may thenbe applied to any 3D model. The gesture generator also eliminates theneed for a skilled animator to implement a gesture. Now, the deformationgrids for the 3D model generation will be described.

FIGS. 1A-10D illustrate exemplary deformation grids that may be used todefine a 3D model 70 of a human head. FIG. 10A illustrates a boundsspace deformation grid 72 which is the innermost deformation grid.Overlaying the bounds space deformation grid 72 is a feature spacedeformation grid 74 (shown in FIG. 10B). An edge space deformation grid76 (show in FIG. 10C) overlays the feature space deformation grid 74.FIG. 10D illustrates a detail deformation grid 7D that is the outermostdeformation grid.

The grids are aligned in accordance with the landmark locations 10(shown in FIG. 10E) such that the head image 30 will be appropriatelyaligned with the deformation grids when its landmark locations 10 arealigned with the landmark locations 10 of the deformation grids. Toproperly align the head image 30 with the deformation grids, a user maymanually refine the landmark location precision on the head image (Step8), for example by using the mouse or other input device to “drag” aparticular landmark to a different area on the image 30. Using the newlandmark location information, the image 30 may be modified with respectto the deformation grids as appropriate (Step 9) in order to properlyalign the head image 30 with the deformation grids. A new model statecan then be calculated, the detail grid 78 can then be detached (Step10), behaviors can be scaled for the resulting 3D model (Step 11), andthe model can be saved (Step 12) for use as a virtual personality. Now,the automatic gesture generation in accordance with the invention willbe described in more detail.

FIG. 11 is a flowchart illustrating an automatic gesture generationmethod 100 in accordance with the invention. In general, the automaticgesture generation results in a gesture object which may then be appliedto any 3D model so that a gesture behavior may be rapidly generated andreused with other models. Usually, there may need to be a separategesture model for different types of 3D models. For example, a smilegesture may need to be automatically generated for a human male, a humanfemale, a human male child and a human female child in order to make thegesture more realistic. The method begins is step 102 in which a commonfeature space is generated. The feature space is common space that isused to store and represent an object image, such as a face, movementsof the object during a gesture and object scalars which capture thedifferences between different objects. The gesture object to begenerated using this method also stores a scalar field variable thatstores the mapping between a model space and the feature space thatpermits transformation of motion and geometry data. The automaticgesture generation method involves using a particular image of anobject, such as a face, to generate an abstraction of a gesture of theobject, such as a smile, which is then stored as a gesture object sothat the gesture object may then be applied to any 3D model.

Returning to FIG. 11, in step 104, the method determines the correlationbetween the feature space and the image space to determine the texturemap changes which represent changes to the surface movements of theimage during the gesture. In step 106, the method updates the texturemap from the image (to check the correlation) and applies the resultanttexture map to the feature space and generates a variable“stDeltaChange” as shown in the exemplary pseudo-code shown in FIGS. 14Aand 14B which stores the texture map changes. In step 108, the methoddetermines the changes in the 3D vertices of the image model during thegesture which captures the 3D movement that occurs during the gesture.In step 110, the vertex changes are applied to the feature space and arecaptured in the gesture object in a variable “VertDeltaChange” as shownin FIGS. 14A and 14B. In step 112, the method determines the texturecoloration that occurs during the gesture and applies it to the featurespace. The texture coloration is captured in the “DeltaMap” variable inthe gesture object. In step 114, the gesture object is generated thatincludes the “stDeltaChange”, “VertDeltaChange” and “DeltaMap” variableswhich contain the coloration, 2D and 3D movement that occurs during thegesture. The variables represent only the movement and color changesthat occurs during a gesture so that the gesture object may then beapplied to any 3D model. In essence, the gesture object distills thegesture that exists in a particular image model into an abstract objectthat contains the essential elements of the gesture so that the gesturemay then be applied to any 3D model.

The gesture object also includes a scalar field variable storing themapping between a feature space of the gesture and a model space of amodel to permit transformation of the geometry and motion data. ThescalarArray has an entry for each geometry vertex in the Gesture object.Each entry is a 3 dimensional vector that holds the change in scale forthat vertex of the Feature level from its undeformed state to thedeformed state. The scale is computed by vertex in Feature space byevaluating the scalar change in distance from that vertex to connectedvertices. The scalar for a given Gesture vertex is computed by weightedinterpolation of that Vertex's position when mapped to UV space of apolygon in the Feature Level. The shape and size of polygons in thefeature level are chosen to match areas of similarly scaled movement.This was determined by analyzing visual flow of typical facial gestures.The above method is shown in greater detail in the pseudo-code shown inFIGS. 14A and 14B.

FIGS. 12A-B and FIGS. 13A and B, respectively, contain a sample pseudocode algorithm and exemplary workflow process for automaticallygenerating a 3D model in accordance with the invention.

The automatically generated model can incorporate built-in behavioranimation and interactivity. For example, for the human face, suchexpressions include gestures, mouth positions for lip syncing (visemes),and head movements. Such behaviors can be integrated with technologiessuch as automatic lip syncing, text-to-speech, natural languageprocessing, and speech recognition and can trigger or be triggered byuser or data driven events. For example, real-time lip syncing ofautomatically generated models may be associated with audio tracks. Inaddition, real-time analysis of the audio spoken by an intelligent agentcan be provided and synchronized head and facial gestures initiated toprovide automatic, lifelike movements to accompany speech delivery.

Thus, virtual personas can be deployed to serve as an intelligent agentthat may be used as an interactive, responsive front-end to informationcontained within knowledge bases, customer resource management systems,and learning management systems, as well as entertainment applicationsand communications via chat, instant messaging, and e-mail. Now,examples of a gesture being generated from an image of a 3D model andthen applied to another model in accordance with the invention will nowdescribed.

FIG. 15 illustrates an example of a base 3D model for a first model,Kristen. The 3D model shown in FIG. 15 has been previously generated asdescribed above using the 3D model generation process. FIG. 16illustrates a second 3D model generated as described above. These twomodels will be used to illustrate the automatic generation of a smilegesture from an existing model to generate a gesture object and then theapplication of that generated gesture object to another 3D model. FIG.17 show an example of the first model in a neutral gesture while FIG. 18shows an example of the first model in a smile gesture. The smilegesture of the first model is then captured as described above. FIG. 19illustrates an example of the smile gesture map (the graphical versionof the gesture object described above) that is generated from the firstmodel based on the neutral gesture and the smile gesture. As describedabove, the gesture map abstracts the gesture behavior of the first modelinto a series of coloration changes, texture map changes and 3D verticeschanges which can then be applied to any other 3D model that has texturemaps and 3D vertices. Then, using this gesture map (which includes thevariables described above), the gesture object may be applied to anothermodel in accordance with the invention. In this manner, the automaticgesture generation process permits various gestures for a 3D model to beabstracted and then applied to other 3D models.

FIG. 20 is an example of the feature space with both the models overlaidover each other to illustrate that the feature space of the first andsecond models are consistent with each other. Now, the application ofthe gesture map (and therefore the gesture object) to another model willbe described in more detail. In particular, FIG. 21 illustrates theneutral gesture of the second model. FIG. 22 illustrates the smilegesture (from the gesture map generated by from the first model) appliedto the second model to provide a smile gesture to that second model evenwhen the second model does not actually show a smile.

While the above has been described with reference to a particular methodfor locating the landmark location points on the image and a particularmethod for generating gestures, those skilled in the art will recognizethat other techniques may be used without departing from the inventionas defined by the appended claims. For example, techniques such aspyramid transforms which uses a frequency analysis of the image by downsampling each level and analyzing the frequency differences at eachlevel can be used. Additionally, other techniques such as side samplingand image pyramid techniques can be used to process the image.Furthermore, quadrature (low pass) filtering techniques may be used toincrease the signal strength of the facial features, and fuzzy logictechniques may be used to identify the overall location of a face. Thelocation of the landmarks may then be determined by a known cornerfinding algorithm.

1. (canceled)
 2. A method comprising: receiving, by a computing device,an image of a first object performing a gesture, the first object havinga surface and a vertex; defining, by the computing device, a gestureobject comprising a two dimensional variable configured to indicate achange associated with the surface in response to the gesture and athree dimensional variable configured to indicate a change associatedwith the vertex in response to the gesture; and applying, by thecomputing device, the gesture object to a model of a second object toanimate the second object.
 3. The method of claim 2, wherein the gestureobject further comprises: a scalar field variable configured to store amapping between a feature space of the gesture and a model space of asecond model to enable transformation of the vertex.
 4. The method ofclaim 3, wherein the gesture object further comprises: a texture changevariable configured to store changes in coloration of the second modelduring the gesture.
 5. The method of claim 3, wherein the scalar fieldvariable comprises an array having respective entries for vertices ofthe gesture, the entries comprising respective three-dimensional vectorsconfigured to store data representing changes in scale of the verticesin the feature space between an undeformed state and a deformed state.6. The method of claim 5, wherein the scalar field variable is based ona weighted interpolation of respective positions of the vertex mapped toa space associated with a polygon in the feature space.
 7. The method ofclaim 5, further comprising automatically generating the gesture object,wherein generating the gesture object includes generating the featurespace and mapping the gesture between the feature space and the modelspace.
 8. A non-transitory computer-readable medium having instructionsstored thereon, the instructions comprising: instructions for receivingan image of a first object performing a gesture, the first object havinga surface and a vertex; instructions for defining a gesture objectcomprising a two dimensional variable configured to indicate a changeassociated with the surface in response to the gesture and a threedimensional variable configured to indicate a change associated with thevertex in response to the gesture; and instructions for applying thegesture object to a model of a second object to animate the secondobject.
 9. The non-transitory computer-readable medium of claim 8,wherein the gesture object further comprises: a scalar field variableconfigured to store a mapping between a feature space of the gesture anda model space of a second model to enable transformation of the vertex.10. The non-transitory computer-readable medium of claim 9, wherein thegesture object further comprises: a texture change variable configuredto store changes in coloration of the second model during the gesture.11. The non-transitory computer-readable medium of claim 9, wherein thescalar field variable comprises an array having respective entries forvertices of the gesture, the entries comprising respectivethree-dimensional vectors configured to store data representing changesin scale of the vertices in the feature space between an undeformedstate and a deformed state.
 12. The non-transitory computer-readablemedium of claim 11, wherein the scalar field variable is based on aweighted interpolation of respective positions of the vertex mapped to aspace associated with a polygon in the feature space.
 13. Thenon-transitory computer-readable medium of claim 11, the instructionsfurther comprising: instructions for automatically generating thegesture object, wherein generating the gesture object includesgenerating the feature space and mapping the gesture between the featurespace and the model space.
 14. An apparatus comprising: means forreceiving an image of a first object performing a gesture, the firstobject having a surface and a vertex; means for defining a gestureobject comprising a two dimensional variable configured to indicate achange associated with the surface in response to the gesture and athree dimensional variable configured to indicate a change associatedwith the vertex in response to the gesture; and means for applying thegesture object to a model of a second object to animate the secondobject.
 15. The apparatus of claim 14, wherein the gesture objectfurther comprises: a scalar field variable configured to store a mappingbetween a feature space of the gesture and a model space of a secondmodel to enable transformation of the vertex.
 16. The apparatus of claim15, wherein the gesture object further comprises: a texture changevariable configured to store changes in coloration of the second modelduring the gesture.
 17. The apparatus of claim 15, wherein the scalarfield variable comprises an array having respective entries for verticesof the gesture, the entries comprising respective three-dimensionalvectors configured to store data representing changes in scale of thevertices in the feature space between an undeformed state and a deformedstate.
 18. The apparatus of claim 17, wherein the scalar field variableis based on a weighted interpolation of respective positions of thevertex mapped to a space associated with a polygon in the feature space.19. The apparatus of claim 17, further comprising: means forautomatically generating the gesture object, wherein generating thegesture object includes generating the feature space and mapping thegesture between the feature space and the model space.